Evolutionary fuzzy clustering of relational data

Danilo Horta*, Ivan C. De Andrade, Ricardo J.G.B. Campello

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Abstract

This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed.

OriginalsprogEngelsk
TidsskriftTheoretical Computer Science
Vol/bind412
Udgave nummer42
Sider (fra-til)5854-5870
ISSN0304-3975
DOI
StatusUdgivet - 30. sep. 2011
Udgivet eksterntJa

Bibliografisk note

Funding Information:
The authors thank CNPq and FAPESP for the financial support.

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